asterix-app/src/test/resources/fuzzyjoin/pub/070-join-aql.aql

use dataverse fuzzy1;

declare type DBLPType as open {
  id: int32, 
  dblpid: string,
  title: string,
  authors: string,
  misc: string
}

declare type CSXType as open {
  id: int32, 
  csxid: string,
  title: string,
  authors: string,
  misc: string
}

declare nodegroup group1 on nc1, nc2;

declare dataset DBLP(DBLPType) 
  primary key id on group1;

declare dataset CSX(CSXType) 
  primary key id on group1;

write output to nc1:'/tmp/pub.adm';

//
// -- - Stage 3 - --
//
for $ridpair in 
    //
    // -- - Stage 2 - --
    //
    for $paperR in dataset('DBLP')
    let $lenR := len(counthashed-word-tokens($paperR.title))
    let $tokensR :=
        for $word in counthashed-word-tokens($paperR.title)
        for $token at $i in
            //
            // -- - Stage 1 - --
            //
            for $paper in dataset('DBLP')
            for $word in counthashed-word-tokens($paper.title)
            group by $item := $word with $paper
            order by count($paper)
            return $item
        where $word = $token
        order by $i
        return $i
    for $prefix_tokenR in subset-collection(
                                $tokensR, 
                                0,
                                prefix-len($lenR, 'Jaccard', .5))

    for $paperS in dataset('CSX')
    let $lenS := len(counthashed-word-tokens($paperS.title))
    let $tokensS :=
        for $word in counthashed-word-tokens($paperS.title)
        for $token at $i in
            //
            // -- - Stage 1 - --
            //
            for $paper in dataset('DBLP')
            for $word in counthashed-word-tokens($paper.title)
            group by $item := $word with $paper
            order by count($paper)
            return $item
        where $word = $token
        order by $i
        return $i
    for $prefix_tokenS in subset-collection(
                                $tokensS, 
                                0,
                                prefix-len($lenS, 'Jaccard', .5))

    where $prefix_tokenR = $prefix_tokenS

    let $sim := similarity(
                    $lenR,
                    $tokensR,
                    $lenS,
                    $tokensS,
                    $prefix_tokenR,
                    'Jaccard',
                    .5)
    where $sim >= .5
    group by $idR := $paperR.id, $idS := $paperS.id with $sim
    return {'idR': $idR, 'idS': $idS, 'sim': $sim[0]}

for $paperR in dataset('DBLP')
for $paperS in dataset('CSX')
where $ridpair.idR = $paperR.id and $ridpair.idS = $paperS.id
return { 'R': { 'dblpid': $paperR.dblpid, 'title': $paperR.title },
         'S': { 'csxid': $paperS.csxid, 'title': $paperS.title },
         'sim': $ridpair.sim }